Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry
File(s)
Published version
Author(s)
Mroz, Austin M
Basford, Annabel R
Hastedt, Friedrich
Jayasekera, Isuru Shavindra
Mosquera-Lois, Irea
Type
Journal Article
Abstract
From accelerating simulations and exploring chemical space, to experimental planning and integrating automation within experimental labs, artificial intelligence (AI) is changing the landscape of chemistry. We are seeing a significant increase in the number of publications leveraging these powerful data-driven insights and models to accelerate all aspects of chemical research. For example, how we represent molecules and materials to computer algorithms for predictive and generative models, as well as the physical mechanisms by which we perform experiments in the lab for automation. Here, we present ten diverse perspectives on the impact of AI coming from those with a range of backgrounds from experimental chemistry, computational chemistry, computer science, engineering and across different areas of chemistry, including drug discovery, catalysis, chemical automation, chemical physics, materials chemistry. The ten perspectives presented here cover a range of themes, including AI for computation, facilitating discovery, supporting experiments, and enabling technologies for transformation. We highlight and discuss imminent challenges and ways in which we are redefining problems to accelerate the impact of chemical research via AI.
Date Issued
2025-06-07
Date Acceptance
2025-04-01
Citation
Chemical Society Reviews, 2025, 54 (11), pp.5433-5469
ISSN
0306-0012
Publisher
Royal Society of Chemistry
Start Page
5433
End Page
5469
Journal / Book Title
Chemical Society Reviews
Volume
54
Issue
11
Copyright Statement
© The Royal Society of Chemistry 2025 Open Access Article. This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/40278836
Subjects
AUTOMATION
BAYESIAN OPTIMIZATION
CHEMICAL LANGUAGE
Chemistry
Chemistry, Multidisciplinary
DESIGN
DISCOVERY
MODELS
Physical Sciences
REPRESENTATIONS
RETROSYNTHESIS
Science & Technology
TRANSFORMER
UNCERTAINTY
Publication Status
Published
Coverage Spatial
England
Date Publish Online
2025-04-25